In [11]:
from __future__ import print_function
import sys
import os
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import os
import glob

#matplotlib.rcParams['savefig.dpi'] = 2 * matplotlib.rcParams['savefig.dpi'] # larger image
#matplotlib.rcParams['figure.figsize']= (20.0, 10.0) # larger image


print("Python version:\n{}\n".format(sys.version))
print("matplotlib version: {}".format(matplotlib.__version__))
print("pandas version: {}".format(pd.__version__))
print("numpy version: {}".format(np.__version__))
print("seaborn version: {}".format(sns.__version__))

Python version:
3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 02:25:08) 
[GCC 7.5.0]

matplotlib version: 3.3.3
pandas version: 1.1.4
numpy version: 1.19.4
seaborn version: 0.11.0
In [12]:
root_path = '../2-Run1'  # The path to the directory containing the systems

igbs = ['igb1', 'igb2', 'igb5', 'igb7', 'igb8']  # Each of the systems as named in their directory

ffs = ['ff94','ff96','ff98','ff99', 'ff99SB', 'ff99SBildn', 'ff99SBnmr', 'ff03.r1', 'ff14SB', \
       'ff14SBonlysc','ff14ipq','fb15','ff15ipq']  # The name of the state directories

filename_pattern = 'helicity.dat'  # The glob to match the data files to read

dfs = {}  # Mapping of system name to list of state DataFrames

igbff_all = []
for ff in ffs:
    #igbff_all = []
    for igb in igbs:
        direc=igb + '_'+ ff
        # For each system and state, grab all the data files that match the glob
        file_glob = os.path.join(root_path, direc, filename_pattern)
        files = glob.glob(file_glob)
        for f in files:
            # Read each into a file to be put into a dict
            df = pd.read_csv(f, sep=r'\s+', skiprows=1, names=['Frame', 'Fraction helicity'])
            df[r'Time ($\mu$s)'] = df['Frame']/10000
            df['Helicity (%)'] = df['Fraction helicity']*100*33/31
            df['Mouving average']=pd.Series.rolling(df['Helicity (%)'],window=500,center=False).mean()
            df['igb'] = igb
            df['ff'] = ff
            df['ffigb'] = '{0} {1}'.format(ff, igb)
            df['igbff'] = '{0} {1}'.format(igb, ff)
            igbff_all.append(df)
df_total = pd.concat(igbff_all)
print(df_total.head())

   Frame  Fraction helicity  Time ($\mu$s)  Helicity (%)  Mouving average  \
0      1              0.848         0.0001     90.270968              NaN   
1      2              0.758         0.0002     80.690323              NaN   
2      3              0.879         0.0003     93.570968              NaN   
3      4              0.848         0.0004     90.270968              NaN   
4      5              0.879         0.0005     93.570968              NaN   

    igb    ff      ffigb      igbff  
0  igb1  ff94  ff94 igb1  igb1 ff94  
1  igb1  ff94  ff94 igb1  igb1 ff94  
2  igb1  ff94  ff94 igb1  igb1 ff94  
3  igb1  ff94  ff94 igb1  igb1 ff94  
4  igb1  ff94  ff94 igb1  igb1 ff94
In [13]:
90.9*33/31
Out[13]:
96.76451612903227
In [14]:
sns.set(context='notebook', style='whitegrid', palette='deep', font='sans-serif',\
        font_scale=1.6, rc={'grid.linestyle': '--'})

grid = sns.FacetGrid(df_total, col="igbff", col_wrap=13, hue="ff", height=5, aspect=1,\
                     sharex=True, sharey=True, despine=False, legend_out=False)


# Draw a horizontal line to show the starting point
grid.map(plt.axhline, y=96.76, ls=":", c=".5") # 90.9*33/31

grid.map(plt.plot, r'Time ($\mu$s)', 'Mouving average').set_titles("{col_name}")
grid.map(plt.plot, r'Time ($\mu$s)', 'Helicity (%)', alpha=0.6).set_titles("{col_name}")
# Adjust the tick positions and labels
#grid.set(xticks=np.arange(5), yticks=[-3, 3],
         #xlim=(-.5, 4.5), ylim=(-3.5, 3.5))

# Adjust the arrangement of the plots
#grid.fig.tight_layout(w_pad=1)
grid.set(xlim=(0, 6) , ylim=(0, 100))
plt.xlabel(r'time ($\mu$s)')
#plt.ylabel('Percentage helicity (%)')
plt.savefig('./plots/helicity.pdf')
# plt.savefig('./plots/helicity.png', dpi=300)
plt.show()
In [15]:
sns.set(context='notebook', style='whitegrid', palette='deep', font='sans-serif',\
        font_scale=1.6, rc={'grid.linestyle': '--'})

grid = sns.FacetGrid(df_total, col="ffigb", col_wrap=5, hue="ffigb", height=5, aspect=1,\
                     sharex=True, sharey=True, despine=False, legend_out=False)


# Draw a horizontal line to show the starting point
grid.map(plt.axhline, y=96.76, ls=":", c=".5") # 90.9*33/31

grid.map(plt.plot, r'Time ($\mu$s)', 'Mouving average').set_titles("{col_name}")
grid.map(plt.plot, r'Time ($\mu$s)', 'Helicity (%)', alpha=0.6).set_titles("{col_name}")
# Adjust the tick positions and labels
#grid.set(xticks=np.arange(5), yticks=[-3, 3],
         #xlim=(-.5, 4.5), ylim=(-3.5, 3.5))

# Adjust the arrangement of the plots
#grid.fig.tight_layout(w_pad=1)
grid.set(xlim=(0, 6) , ylim=(0, 100))
plt.xlabel(r'time ($\mu$s)')
#plt.ylabel('Percentage helicity (%)')
plt.savefig('./plots/helicity2.pdf')
# plt.savefig('./plots/helicity2.png', dpi=300)
plt.show()
In [10]:
df_total
Out[10]:
Frame Fraction helicity Time ($\mu$s) Helicity (%) Mouving average igb ff igbff
0 1 0.848 0.0001 90.270968 NaN igb1 ff94 ff94 igb1
1 2 0.758 0.0002 80.690323 NaN igb1 ff94 ff94 igb1
2 3 0.879 0.0003 93.570968 NaN igb1 ff94 ff94 igb1
3 4 0.848 0.0004 90.270968 NaN igb1 ff94 ff94 igb1
4 5 0.879 0.0005 93.570968 NaN igb1 ff94 ff94 igb1
5 6 0.758 0.0006 80.690323 NaN igb1 ff94 ff94 igb1
6 7 0.818 0.0007 87.077419 NaN igb1 ff94 ff94 igb1
7 8 0.788 0.0008 83.883871 NaN igb1 ff94 ff94 igb1
8 9 0.848 0.0009 90.270968 NaN igb1 ff94 ff94 igb1
9 10 0.848 0.0010 90.270968 NaN igb1 ff94 ff94 igb1
10 11 0.758 0.0011 80.690323 NaN igb1 ff94 ff94 igb1
11 12 0.697 0.0012 74.196774 NaN igb1 ff94 ff94 igb1
12 13 0.818 0.0013 87.077419 NaN igb1 ff94 ff94 igb1
13 14 0.848 0.0014 90.270968 NaN igb1 ff94 ff94 igb1
14 15 0.788 0.0015 83.883871 NaN igb1 ff94 ff94 igb1
15 16 0.848 0.0016 90.270968 NaN igb1 ff94 ff94 igb1
16 17 0.848 0.0017 90.270968 NaN igb1 ff94 ff94 igb1
17 18 0.818 0.0018 87.077419 NaN igb1 ff94 ff94 igb1
18 19 0.727 0.0019 77.390323 NaN igb1 ff94 ff94 igb1
19 20 0.727 0.0020 77.390323 NaN igb1 ff94 ff94 igb1
20 21 0.697 0.0021 74.196774 NaN igb1 ff94 ff94 igb1
21 22 0.848 0.0022 90.270968 NaN igb1 ff94 ff94 igb1
22 23 0.788 0.0023 83.883871 NaN igb1 ff94 ff94 igb1
23 24 0.818 0.0024 87.077419 NaN igb1 ff94 ff94 igb1
24 25 0.879 0.0025 93.570968 NaN igb1 ff94 ff94 igb1
25 26 0.788 0.0026 83.883871 NaN igb1 ff94 ff94 igb1
26 27 0.818 0.0027 87.077419 NaN igb1 ff94 ff94 igb1
27 28 0.818 0.0028 87.077419 NaN igb1 ff94 ff94 igb1
28 29 0.788 0.0029 83.883871 NaN igb1 ff94 ff94 igb1
29 30 0.879 0.0030 93.570968 NaN igb1 ff94 ff94 igb1
... ... ... ... ... ... ... ... ...
59970 59971 0.273 5.9971 29.061290 26.679542 igb8 ff15ipq ff15ipq igb8
59971 59972 0.333 5.9972 35.448387 26.679542 igb8 ff15ipq ff15ipq igb8
59972 59973 0.121 5.9973 12.880645 26.615032 igb8 ff15ipq ff15ipq igb8
59973 59974 0.242 5.9974 25.761290 26.602045 igb8 ff15ipq ff15ipq igb8
59974 59975 0.242 5.9975 25.761290 26.576071 igb8 ff15ipq ff15ipq igb8
59975 59976 0.242 5.9976 25.761290 26.601832 igb8 ff15ipq ff15ipq igb8
59976 59977 0.242 5.9977 25.761290 26.627594 igb8 ff15ipq ff15ipq igb8
59977 59978 0.121 5.9978 12.880645 26.601832 igb8 ff15ipq ff15ipq igb8
59978 59979 0.242 5.9979 25.761290 26.627594 igb8 ff15ipq ff15ipq igb8
59979 59980 0.121 5.9980 12.880645 26.627594 igb8 ff15ipq ff15ipq igb8
59980 59981 0.303 5.9981 32.254839 26.666342 igb8 ff15ipq ff15ipq igb8
59981 59982 0.424 5.9982 45.135484 26.756613 igb8 ff15ipq ff15ipq igb8
59982 59983 0.424 5.9983 45.135484 26.821123 igb8 ff15ipq ff15ipq igb8
59983 59984 0.303 5.9984 32.254839 26.840497 igb8 ff15ipq ff15ipq igb8
59984 59985 0.455 5.9985 48.435484 26.911606 igb8 ff15ipq ff15ipq igb8
59985 59986 0.455 5.9986 48.435484 26.982716 igb8 ff15ipq ff15ipq igb8
59986 59987 0.424 5.9987 45.135484 27.027852 igb8 ff15ipq ff15ipq igb8
59987 59988 0.273 5.9988 29.061290 27.085974 igb8 ff15ipq ff15ipq igb8
59988 59989 0.455 5.9989 48.435484 27.182845 igb8 ff15ipq ff15ipq igb8
59989 59990 0.394 5.9990 41.941935 27.240968 igb8 ff15ipq ff15ipq igb8
59990 59991 0.242 5.9991 25.761290 27.266729 igb8 ff15ipq ff15ipq igb8
59991 59992 0.273 5.9992 29.061290 27.247355 igb8 ff15ipq ff15ipq igb8
59992 59993 0.121 5.9993 12.880645 27.195619 igb8 ff15ipq ff15ipq igb8
59993 59994 0.121 5.9994 12.880645 27.137497 igb8 ff15ipq ff15ipq igb8
59994 59995 0.273 5.9995 29.061290 27.098748 igb8 ff15ipq ff15ipq igb8
59995 59996 0.242 5.9996 25.761290 27.079374 igb8 ff15ipq ff15ipq igb8
59996 59997 0.152 5.9997 16.180645 27.066600 igb8 ff15ipq ff15ipq igb8
59997 59998 0.394 5.9998 41.941935 27.124723 igb8 ff15ipq ff15ipq igb8
59998 59999 0.485 5.9999 51.629032 27.176458 igb8 ff15ipq ff15ipq igb8
59999 60000 0.424 6.0000 45.135484 27.195832 igb8 ff15ipq ff15ipq igb8

3104774 rows × 8 columns

In [11]:
temp=df_total.drop(columns=['Frame', 'Fraction helicity', r'Time ($\mu$s)', 'Mouving average', 'igb','ff' ])
temp
Out[11]:
Helicity (%) igbff
0 90.270968 ff94 igb1
1 80.690323 ff94 igb1
2 93.570968 ff94 igb1
3 90.270968 ff94 igb1
4 93.570968 ff94 igb1
5 80.690323 ff94 igb1
6 87.077419 ff94 igb1
7 83.883871 ff94 igb1
8 90.270968 ff94 igb1
9 90.270968 ff94 igb1
10 80.690323 ff94 igb1
11 74.196774 ff94 igb1
12 87.077419 ff94 igb1
13 90.270968 ff94 igb1
14 83.883871 ff94 igb1
15 90.270968 ff94 igb1
16 90.270968 ff94 igb1
17 87.077419 ff94 igb1
18 77.390323 ff94 igb1
19 77.390323 ff94 igb1
20 74.196774 ff94 igb1
21 90.270968 ff94 igb1
22 83.883871 ff94 igb1
23 87.077419 ff94 igb1
24 93.570968 ff94 igb1
25 83.883871 ff94 igb1
26 87.077419 ff94 igb1
27 87.077419 ff94 igb1
28 83.883871 ff94 igb1
29 93.570968 ff94 igb1
... ... ...
59970 29.061290 ff15ipq igb8
59971 35.448387 ff15ipq igb8
59972 12.880645 ff15ipq igb8
59973 25.761290 ff15ipq igb8
59974 25.761290 ff15ipq igb8
59975 25.761290 ff15ipq igb8
59976 25.761290 ff15ipq igb8
59977 12.880645 ff15ipq igb8
59978 25.761290 ff15ipq igb8
59979 12.880645 ff15ipq igb8
59980 32.254839 ff15ipq igb8
59981 45.135484 ff15ipq igb8
59982 45.135484 ff15ipq igb8
59983 32.254839 ff15ipq igb8
59984 48.435484 ff15ipq igb8
59985 48.435484 ff15ipq igb8
59986 45.135484 ff15ipq igb8
59987 29.061290 ff15ipq igb8
59988 48.435484 ff15ipq igb8
59989 41.941935 ff15ipq igb8
59990 25.761290 ff15ipq igb8
59991 29.061290 ff15ipq igb8
59992 12.880645 ff15ipq igb8
59993 12.880645 ff15ipq igb8
59994 29.061290 ff15ipq igb8
59995 25.761290 ff15ipq igb8
59996 16.180645 ff15ipq igb8
59997 41.941935 ff15ipq igb8
59998 51.629032 ff15ipq igb8
59999 45.135484 ff15ipq igb8

3104774 rows × 2 columns

In [12]:
grouped=temp.groupby(['igbff'])
grouped
Out[12]:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fe8b4dba4e0>
In [13]:
grouped.std()
Out[13]:
Helicity (%)
igbff
fb15 igb1 3.059155
fb15 igb2 0.784488
fb15 igb5 2.249720
fb15 igb8 0.460499
ff03.r1 igb1 7.873248
ff03.r1 igb2 14.943242
ff03.r1 igb5 15.562923
ff03.r1 igb8 9.305667
ff14SB igb1 18.902845
ff14SB igb2 17.767110
ff14SB igb5 15.807232
ff14SB igb8 16.818900
ff14SBonlysc igb1 9.518143
ff14SBonlysc igb2 16.154871
ff14SBonlysc igb5 13.551569
ff14SBonlysc igb8 15.059350
ff14ipq igb1 11.359988
ff14ipq igb2 10.748451
ff14ipq igb5 12.991186
ff14ipq igb8 18.559135
ff15ipq igb1 13.051642
ff15ipq igb2 12.870617
ff15ipq igb5 10.946475
ff15ipq igb8 15.001460
ff94 igb1 13.954824
ff94 igb2 10.879590
ff94 igb5 8.540923
ff94 igb8 10.370183
ff96 igb1 10.806351
ff96 igb2 16.516173
ff96 igb5 8.118670
ff96 igb8 22.708779
ff98 igb1 12.132487
ff98 igb2 10.940129
ff98 igb5 8.578049
ff98 igb8 10.578163
ff99 igb1 13.408737
ff99 igb2 16.480980
ff99 igb5 17.575457
ff99 igb8 14.371592
ff99SB igb1 13.254608
ff99SB igb2 15.918683
ff99SB igb5 18.856696
ff99SB igb8 16.730622
ff99SBildn igb1 15.019689
ff99SBildn igb2 16.269780
ff99SBildn igb5 17.945899
ff99SBildn igb8 16.674803
ff99SBnmr igb1 12.085722
ff99SBnmr igb2 8.303788
ff99SBnmr igb5 6.428906
ff99SBnmr igb8 8.301609
In [14]:
grouped.mean()
Out[14]:
Helicity (%)
igbff
fb15 igb1 0.321392
fb15 igb2 0.026015
fb15 igb5 0.211177
fb15 igb8 0.006447
ff03.r1 igb1 83.940136
ff03.r1 igb2 68.102338
ff03.r1 igb5 70.983365
ff03.r1 igb8 79.301810
ff14SB igb1 52.776490
ff14SB igb2 57.929454
ff14SB igb5 67.430016
ff14SB igb8 63.073386
ff14SBonlysc igb1 30.057411
ff14SBonlysc igb2 21.157150
ff14SBonlysc igb5 27.908725
ff14SBonlysc igb8 25.354438
ff14ipq igb1 28.604404
ff14ipq igb2 31.218869
ff14ipq igb5 13.053727
ff14ipq igb8 26.178842
ff15ipq igb1 48.139873
ff15ipq igb2 13.253098
ff15ipq igb5 46.665000
ff15ipq igb8 33.839284
ff94 igb1 73.499564
ff94 igb2 77.528502
ff94 igb5 82.448064
ff94 igb8 79.843894
ff96 igb1 76.846259
ff96 igb2 5.348658
ff96 igb5 80.626895
ff96 igb8 9.163818
ff98 igb1 79.081156
ff98 igb2 77.457559
ff98 igb5 82.326658
ff98 igb8 79.582866
ff99 igb1 20.381254
ff99 igb2 32.484096
ff99 igb5 40.658693
ff99 igb8 29.912502
ff99SB igb1 30.117296
ff99SB igb2 20.742663
ff99SB igb5 34.185133
ff99SB igb8 26.584784
ff99SBildn igb1 25.318170
ff99SBildn igb2 19.602209
ff99SBildn igb5 32.575911
ff99SBildn igb8 20.770715
ff99SBnmr igb1 78.941021
ff99SBnmr igb2 83.385583
ff99SBnmr igb5 86.866411
ff99SBnmr igb8 82.960079
In [15]:
mean = grouped.mean()

errors = grouped.std()


ax=mean.plot(yerr=errors, kind='bar',figsize=(25,10), ylim=(0, 100), legend=False, rot=90 )
ax.set_ylabel('Helicity (%)')
ax.set_xlabel('Implicit solvent / force field combinations')
plt.axhline(y=96.76, ls=":", c=".5" ) # 90.9*33/31

plt.tight_layout()
plt.savefig('./helicity_avg.pdf')
plt.savefig('./helicity_avg.png')
#plt.show()
In [16]:
sns.set(context='paper', style='whitegrid', palette='deep', font='sans-serif',\
        font_scale=1.6, rc={'grid.linestyle': '--'})

fig, axes = plt.subplots(figsize=(12, 10))

ax=sns.boxplot(x='igbff', y='Helicity (%)', data=df_total, fliersize=0)
ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
ax.set_ylim([0, 100])  
ax.set_xlabel('Implicit solvent / force field combinations')
plt.axhline(y=96.76, ls=":", c=".5" ) # 90.9*33/31

plt.tight_layout()
#plt.savefig('./helicity_boxplot.pdf')
plt.savefig('./helicity_boxplot.png', dpi=600)
In [12]:
grouped.boxplot(by='igbff')
Out[12]:
fb15 igb1                 AxesSubplot(0.1,0.820213;0.097561x0.0797872)
fb15 igb2            AxesSubplot(0.217073,0.820213;0.097561x0.0797872)
fb15 igb5            AxesSubplot(0.334146,0.820213;0.097561x0.0797872)
fb15 igb8             AxesSubplot(0.45122,0.820213;0.097561x0.0797872)
ff03.r1 igb1         AxesSubplot(0.568293,0.820213;0.097561x0.0797872)
ff03.r1 igb2         AxesSubplot(0.685366,0.820213;0.097561x0.0797872)
ff03.r1 igb5         AxesSubplot(0.802439,0.820213;0.097561x0.0797872)
ff03.r1 igb8              AxesSubplot(0.1,0.724468;0.097561x0.0797872)
ff14SB igb1          AxesSubplot(0.217073,0.724468;0.097561x0.0797872)
ff14SB igb2          AxesSubplot(0.334146,0.724468;0.097561x0.0797872)
ff14SB igb5           AxesSubplot(0.45122,0.724468;0.097561x0.0797872)
ff14SB igb8          AxesSubplot(0.568293,0.724468;0.097561x0.0797872)
ff14SBonlysc igb1    AxesSubplot(0.685366,0.724468;0.097561x0.0797872)
ff14SBonlysc igb2    AxesSubplot(0.802439,0.724468;0.097561x0.0797872)
ff14SBonlysc igb5         AxesSubplot(0.1,0.628723;0.097561x0.0797872)
ff14SBonlysc igb8    AxesSubplot(0.217073,0.628723;0.097561x0.0797872)
ff14ipq igb1         AxesSubplot(0.334146,0.628723;0.097561x0.0797872)
ff14ipq igb2          AxesSubplot(0.45122,0.628723;0.097561x0.0797872)
ff14ipq igb5         AxesSubplot(0.568293,0.628723;0.097561x0.0797872)
ff14ipq igb8         AxesSubplot(0.685366,0.628723;0.097561x0.0797872)
ff15ipq igb1         AxesSubplot(0.802439,0.628723;0.097561x0.0797872)
ff15ipq igb2              AxesSubplot(0.1,0.532979;0.097561x0.0797872)
ff15ipq igb5         AxesSubplot(0.217073,0.532979;0.097561x0.0797872)
ff15ipq igb8         AxesSubplot(0.334146,0.532979;0.097561x0.0797872)
ff94 igb1             AxesSubplot(0.45122,0.532979;0.097561x0.0797872)
ff94 igb2            AxesSubplot(0.568293,0.532979;0.097561x0.0797872)
ff94 igb5            AxesSubplot(0.685366,0.532979;0.097561x0.0797872)
ff94 igb8            AxesSubplot(0.802439,0.532979;0.097561x0.0797872)
ff96 igb1                 AxesSubplot(0.1,0.437234;0.097561x0.0797872)
ff96 igb2            AxesSubplot(0.217073,0.437234;0.097561x0.0797872)
ff96 igb5            AxesSubplot(0.334146,0.437234;0.097561x0.0797872)
ff96 igb8             AxesSubplot(0.45122,0.437234;0.097561x0.0797872)
ff98 igb1            AxesSubplot(0.568293,0.437234;0.097561x0.0797872)
ff98 igb2            AxesSubplot(0.685366,0.437234;0.097561x0.0797872)
ff98 igb5            AxesSubplot(0.802439,0.437234;0.097561x0.0797872)
ff98 igb8                 AxesSubplot(0.1,0.341489;0.097561x0.0797872)
ff99 igb1            AxesSubplot(0.217073,0.341489;0.097561x0.0797872)
ff99 igb2            AxesSubplot(0.334146,0.341489;0.097561x0.0797872)
ff99 igb5             AxesSubplot(0.45122,0.341489;0.097561x0.0797872)
ff99 igb8            AxesSubplot(0.568293,0.341489;0.097561x0.0797872)
ff99SB igb1          AxesSubplot(0.685366,0.341489;0.097561x0.0797872)
ff99SB igb2          AxesSubplot(0.802439,0.341489;0.097561x0.0797872)
ff99SB igb5               AxesSubplot(0.1,0.245745;0.097561x0.0797872)
ff99SB igb8          AxesSubplot(0.217073,0.245745;0.097561x0.0797872)
ff99SBildn igb1      AxesSubplot(0.334146,0.245745;0.097561x0.0797872)
ff99SBildn igb2       AxesSubplot(0.45122,0.245745;0.097561x0.0797872)
ff99SBildn igb5      AxesSubplot(0.568293,0.245745;0.097561x0.0797872)
ff99SBildn igb8      AxesSubplot(0.685366,0.245745;0.097561x0.0797872)
ff99SBnmr igb1       AxesSubplot(0.802439,0.245745;0.097561x0.0797872)
ff99SBnmr igb2                AxesSubplot(0.1,0.15;0.097561x0.0797872)
ff99SBnmr igb5           AxesSubplot(0.217073,0.15;0.097561x0.0797872)
ff99SBnmr igb8           AxesSubplot(0.334146,0.15;0.097561x0.0797872)
dtype: object
In [13]:
avg=mean.rename(columns={'Helicity (%)':'Mean'})
std=errors.rename(columns={'Helicity (%)':'Std'})
df1=pd.concat([avg,std], axis=1)
df1['igbff']=df1.index.astype('str') 
df1['Implicit solvent / Force field combination'] = df1['igbff'].astype('str') 
df1.dtypes
Out[13]:
Mean                                          float64
Std                                           float64
igbff                                          object
Implicit solvent / Force field combination     object
dtype: object
In [14]:
plt.figure()
plt.errorbar(x= y=mean, yerr=errors)

  File "<ipython-input-14-1d0f2cff0a76>", line 2
    plt.errorbar(x= y=mean, yerr=errors)
                     ^
SyntaxError: invalid syntax
In [ ]:
fig, axes = plt.subplots(1)
df1.plot( y='Mean', yerr='Std', ax=axes, kind='bar')

#axes.set(xlim=(3.0, 10.5) , ylim=(0, 1))

#lgd = plt.legend(bbox_to_anchor=(1.05, 1), loc="lower left", borderaxespad=0., labelspacing=0.2)
axes.set_ylabel('Helicity (%)')
In [ ]: